Overview

Dataset statistics

Number of variables39
Number of observations1880465
Missing cells19788951
Missing cells (%)27.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory559.5 MiB
Average record size in memory312.0 B

Variable types

Numeric14
Categorical24
Unsupported1

Alerts

FPA_ID has a high cardinality: 1880462 distinct values High cardinality
NWCG_REPORTING_UNIT_ID has a high cardinality: 1640 distinct values High cardinality
NWCG_REPORTING_UNIT_NAME has a high cardinality: 1635 distinct values High cardinality
SOURCE_REPORTING_UNIT has a high cardinality: 4992 distinct values High cardinality
SOURCE_REPORTING_UNIT_NAME has a high cardinality: 4441 distinct values High cardinality
LOCAL_FIRE_REPORT_ID has a high cardinality: 13508 distinct values High cardinality
LOCAL_INCIDENT_ID has a high cardinality: 565914 distinct values High cardinality
FIRE_CODE has a high cardinality: 172446 distinct values High cardinality
FIRE_NAME has a high cardinality: 493633 distinct values High cardinality
ICS_209_INCIDENT_NUMBER has a high cardinality: 22737 distinct values High cardinality
ICS_209_NAME has a high cardinality: 19573 distinct values High cardinality
MTBS_ID has a high cardinality: 10481 distinct values High cardinality
MTBS_FIRE_NAME has a high cardinality: 8133 distinct values High cardinality
COMPLEX_NAME has a high cardinality: 1416 distinct values High cardinality
CONT_TIME has a high cardinality: 1441 distinct values High cardinality
STATE has a high cardinality: 52 distinct values High cardinality
COUNTY has a high cardinality: 3455 distinct values High cardinality
FIPS_NAME has a high cardinality: 1698 distinct values High cardinality
OBJECTID is highly correlated with FOD_ID and 3 other fieldsHigh correlation
FOD_ID is highly correlated with OBJECTID and 3 other fieldsHigh correlation
FIRE_YEAR is highly correlated with OBJECTID and 3 other fieldsHigh correlation
DISCOVERY_DATE is highly correlated with OBJECTID and 3 other fieldsHigh correlation
DISCOVERY_DOY is highly correlated with CONT_DOYHigh correlation
CONT_DATE is highly correlated with OBJECTID and 3 other fieldsHigh correlation
CONT_DOY is highly correlated with DISCOVERY_DOYHigh correlation
OBJECTID is highly correlated with FOD_ID and 3 other fieldsHigh correlation
FOD_ID is highly correlated with OBJECTID and 3 other fieldsHigh correlation
FIRE_YEAR is highly correlated with OBJECTID and 3 other fieldsHigh correlation
DISCOVERY_DATE is highly correlated with OBJECTID and 3 other fieldsHigh correlation
DISCOVERY_DOY is highly correlated with CONT_DOYHigh correlation
CONT_DATE is highly correlated with OBJECTID and 3 other fieldsHigh correlation
CONT_DOY is highly correlated with DISCOVERY_DOYHigh correlation
OBJECTID is highly correlated with FOD_ID and 1 other fieldsHigh correlation
FOD_ID is highly correlated with OBJECTID and 1 other fieldsHigh correlation
FIRE_YEAR is highly correlated with DISCOVERY_DATE and 1 other fieldsHigh correlation
DISCOVERY_DATE is highly correlated with FIRE_YEAR and 1 other fieldsHigh correlation
DISCOVERY_DOY is highly correlated with CONT_DOYHigh correlation
CONT_DATE is highly correlated with OBJECTID and 3 other fieldsHigh correlation
CONT_DOY is highly correlated with DISCOVERY_DOYHigh correlation
NWCG_REPORTING_AGENCY is highly correlated with SOURCE_SYSTEM_TYPE and 2 other fieldsHigh correlation
SOURCE_SYSTEM_TYPE is highly correlated with NWCG_REPORTING_AGENCY and 3 other fieldsHigh correlation
OWNER_DESCR is highly correlated with NWCG_REPORTING_AGENCY and 1 other fieldsHigh correlation
SOURCE_SYSTEM is highly correlated with NWCG_REPORTING_AGENCY and 2 other fieldsHigh correlation
STATE is highly correlated with SOURCE_SYSTEM_TYPE and 1 other fieldsHigh correlation
OBJECTID is highly correlated with FOD_ID and 12 other fieldsHigh correlation
FOD_ID is highly correlated with OBJECTID and 5 other fieldsHigh correlation
SOURCE_SYSTEM_TYPE is highly correlated with OBJECTID and 9 other fieldsHigh correlation
SOURCE_SYSTEM is highly correlated with OBJECTID and 13 other fieldsHigh correlation
NWCG_REPORTING_AGENCY is highly correlated with OBJECTID and 7 other fieldsHigh correlation
FIRE_YEAR is highly correlated with OBJECTID and 4 other fieldsHigh correlation
DISCOVERY_DATE is highly correlated with OBJECTID and 4 other fieldsHigh correlation
DISCOVERY_DOY is highly correlated with CONT_DOY and 2 other fieldsHigh correlation
STAT_CAUSE_CODE is highly correlated with OBJECTID and 8 other fieldsHigh correlation
STAT_CAUSE_DESCR is highly correlated with SOURCE_SYSTEM_TYPE and 3 other fieldsHigh correlation
CONT_DATE is highly correlated with OBJECTID and 5 other fieldsHigh correlation
CONT_DOY is highly correlated with DISCOVERY_DOY and 3 other fieldsHigh correlation
LATITUDE is highly correlated with OBJECTID and 6 other fieldsHigh correlation
LONGITUDE is highly correlated with OBJECTID and 10 other fieldsHigh correlation
OWNER_CODE is highly correlated with OBJECTID and 7 other fieldsHigh correlation
OWNER_DESCR is highly correlated with OBJECTID and 6 other fieldsHigh correlation
STATE is highly correlated with OBJECTID and 14 other fieldsHigh correlation
FIPS_CODE is highly correlated with STATEHigh correlation
LOCAL_FIRE_REPORT_ID has 1459286 (77.6%) missing values Missing
LOCAL_INCIDENT_ID has 820821 (43.6%) missing values Missing
FIRE_CODE has 1555636 (82.7%) missing values Missing
FIRE_NAME has 957189 (50.9%) missing values Missing
ICS_209_INCIDENT_NUMBER has 1854748 (98.6%) missing values Missing
ICS_209_NAME has 1854748 (98.6%) missing values Missing
MTBS_ID has 1869462 (99.4%) missing values Missing
MTBS_FIRE_NAME has 1869462 (99.4%) missing values Missing
COMPLEX_NAME has 1875282 (99.7%) missing values Missing
DISCOVERY_TIME has 882638 (46.9%) missing values Missing
CONT_DATE has 891531 (47.4%) missing values Missing
CONT_DOY has 891531 (47.4%) missing values Missing
CONT_TIME has 972173 (51.7%) missing values Missing
COUNTY has 678148 (36.1%) missing values Missing
FIPS_CODE has 678148 (36.1%) missing values Missing
FIPS_NAME has 678148 (36.1%) missing values Missing
FIRE_SIZE is highly skewed (γ1 = 106.83733) Skewed
OBJECTID is uniformly distributed Uniform
FPA_ID is uniformly distributed Uniform
MTBS_ID is uniformly distributed Uniform
OBJECTID has unique values Unique
FOD_ID has unique values Unique
Shape is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-03-08 08:30:52.109457
Analysis finished2022-03-08 08:44:36.458457
Duration13 minutes and 44.35 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1880465
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean940233
Minimum1
Maximum1880465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:36.543195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile94024.2
Q1470117
median940233
Q31410349
95-th percentile1786441.8
Maximum1880465
Range1880464
Interquartile range (IQR)940232

Descriptive statistics

Standard deviation542843.6313
Coefficient of variation (CV)0.5773501157
Kurtosis-1.2
Mean940233
Median Absolute Deviation (MAD)470116
Skewness1.089334184 × 10-15
Sum1.768075248 × 1012
Variance2.946792081 × 1011
MonotonicityStrictly increasing
2022-03-08T09:44:36.687924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
12536541
 
< 0.1%
12536521
 
< 0.1%
12536511
 
< 0.1%
12536501
 
< 0.1%
12536491
 
< 0.1%
12536481
 
< 0.1%
12536471
 
< 0.1%
12536461
 
< 0.1%
12536451
 
< 0.1%
Other values (1880455)1880455
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
18804651
< 0.1%
18804641
< 0.1%
18804631
< 0.1%
18804621
< 0.1%
18804611
< 0.1%
18804601
< 0.1%
18804591
< 0.1%
18804581
< 0.1%
18804571
< 0.1%
18804561
< 0.1%

FOD_ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1880465
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54840199.02
Minimum1
Maximum300348399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:36.852378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile95062.2
Q1505500
median1067761
Q319106386
95-th percentile300146219.8
Maximum300348399
Range300348398
Interquartile range (IQR)18600886

Descriptive statistics

Standard deviation101196328.6
Coefficient of variation (CV)1.8452947
Kurtosis0.5848193526
Mean54840199.02
Median Absolute Deviation (MAD)700448
Skewness1.511181964
Sum1.031250749 × 1014
Variance1.024069692 × 1016
MonotonicityNot monotonic
2022-03-08T09:44:36.998381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
16568741
 
< 0.1%
16568721
 
< 0.1%
16568711
 
< 0.1%
16568701
 
< 0.1%
16568691
 
< 0.1%
16568681
 
< 0.1%
16568671
 
< 0.1%
16568661
 
< 0.1%
16568651
 
< 0.1%
Other values (1880455)1880455
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
3003483991
< 0.1%
3003483771
< 0.1%
3003483751
< 0.1%
3003483731
< 0.1%
3003483631
< 0.1%
3003483621
< 0.1%
3003483611
< 0.1%
3003483541
< 0.1%
3003483281
< 0.1%
3003483111
< 0.1%

FPA_ID
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1880462
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
SFO-2015CACDFLNU003791
 
2
FS-1452833
 
2
ICS209_2009_KS-DDQ-128
 
2
FS-1418826
 
1
SFO-GA-FY2002-Dodge-092
 
1
Other values (1880457)
1880457 

Length

Max length49
Median length16
Mean length16.53853382
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1880459 ?
Unique (%)> 99.9%

Sample

1st rowFS-1418826
2nd rowFS-1418827
3rd rowFS-1418835
4th rowFS-1418845
5th rowFS-1418847

Common Values

ValueCountFrequency (%)
SFO-2015CACDFLNU0037912
 
< 0.1%
FS-14528332
 
< 0.1%
ICS209_2009_KS-DDQ-1282
 
< 0.1%
FS-14188261
 
< 0.1%
SFO-GA-FY2002-Dodge-0921
 
< 0.1%
SFO-GA-FY2002-Dodge-1031
 
< 0.1%
SFO-GA-FY2002-Dodge-1021
 
< 0.1%
SFO-GA-FY2002-Dodge-1011
 
< 0.1%
SFO-GA-FY2002-Dodge-1001
 
< 0.1%
SFO-GA-FY2002-Dodge-0991
 
< 0.1%
Other values (1880452)1880452
> 99.9%

Length

2022-03-08T09:44:37.274889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sfo-2013ladaf1022
 
0.1%
sfo-2010-vavas972
 
0.1%
sfo-2014ladaf817
 
< 0.1%
sfo-2014vavas787
 
< 0.1%
2011vavas767
 
< 0.1%
sfo-2015vavas565
 
< 0.1%
sfo-2013vavas555
 
< 0.1%
sfo-ga-fy2001-bryan97
 
< 0.1%
2011gagas-fy2011-jeff88
 
< 0.1%
sfo-ga-fy2002-bryan84
 
< 0.1%
Other values (1879467)1881700
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOURCE_SYSTEM_TYPE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
NONFED
1362148 
FED
481106 
INTERAGCY
 
37211

Length

Max length9
Median length6
Mean length5.291832073
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFED
2nd rowFED
3rd rowFED
4th rowFED
5th rowFED

Common Values

ValueCountFrequency (%)
NONFED1362148
72.4%
FED481106
 
25.6%
INTERAGCY37211
 
2.0%

Length

2022-03-08T09:44:37.411373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-08T09:44:37.487891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
nonfed1362148
72.4%
fed481106
 
25.6%
interagcy37211
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOURCE_SYSTEM
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
ST-NASF
711236 
DOI-WFMI
241423 
FS-FIRESTAT
220356 
ST-CACDF
87355 
ST-NCNCS
 
65695
Other values (33)
554400 

Length

Max length11
Median length8
Mean length7.98596996
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFS-FIRESTAT
2nd rowFS-FIRESTAT
3rd rowFS-FIRESTAT
4th rowFS-FIRESTAT
5th rowFS-FIRESTAT

Common Values

ValueCountFrequency (%)
ST-NASF711236
37.8%
DOI-WFMI241423
 
12.8%
FS-FIRESTAT220356
 
11.7%
ST-CACDF87355
 
4.6%
ST-NCNCS65695
 
3.5%
ST-GAGAS65061
 
3.5%
ST-MSMSS60513
 
3.2%
ST-TXTXS57945
 
3.1%
ST-ALALS54951
 
2.9%
ST-SCSCS49281
 
2.6%
Other values (28)266649
 
14.2%

Length

2022-03-08T09:44:37.566430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st-nasf711236
37.8%
doi-wfmi241423
 
12.8%
fs-firestat220356
 
11.7%
st-cacdf87355
 
4.6%
st-ncncs65695
 
3.5%
st-gagas65061
 
3.5%
st-msmss60513
 
3.2%
st-txtxs57945
 
3.1%
st-alals54951
 
2.9%
st-scscs49281
 
2.6%
Other values (28)266649
 
14.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NWCG_REPORTING_AGENCY
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
ST/C&L
1377090 
FS
220497 
BIA
 
119943
BLM
 
97034
IA
 
21841
Other values (6)
 
44060

Length

Max length6
Median length6
Mean length5.07204601
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFS
2nd rowFS
3rd rowFS
4th rowFS
5th rowFS

Common Values

ValueCountFrequency (%)
ST/C&L1377090
73.2%
FS220497
 
11.7%
BIA119943
 
6.4%
BLM97034
 
5.2%
IA21841
 
1.2%
NPS20893
 
1.1%
FWS19331
 
1.0%
TRIBE3739
 
0.2%
DOD81
 
< 0.1%
BOR14
 
< 0.1%

Length

2022-03-08T09:44:37.684511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st/c&l1377090
73.2%
fs220497
 
11.7%
bia119943
 
6.4%
blm97034
 
5.2%
ia21841
 
1.2%
nps20893
 
1.1%
fws19331
 
1.0%
tribe3739
 
0.2%
dod81
 
< 0.1%
bor14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NWCG_REPORTING_UNIT_ID
Categorical

HIGH CARDINALITY

Distinct1640
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
USGAGAS
167123 
USTXTXS
 
111362
USNCNCS
 
107424
USFLFLS
 
83024
USSCSCS
 
78977
Other values (1635)
1332555 

Length

Max length9
Median length7
Mean length7.0303409
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)< 0.1%

Sample

1st rowUSCAPNF
2nd rowUSCAENF
3rd rowUSCAENF
4th rowUSCAENF
5th rowUSCAENF

Common Values

ValueCountFrequency (%)
USGAGAS167123
 
8.9%
USTXTXS111362
 
5.9%
USNCNCS107424
 
5.7%
USFLFLS83024
 
4.4%
USSCSCS78977
 
4.2%
USNYNYX75461
 
4.0%
USMSMSS75117
 
4.0%
USALALS64705
 
3.4%
USOKOKS29682
 
1.6%
USMNMNS29432
 
1.6%
Other values (1630)1058158
56.3%

Length

2022-03-08T09:44:37.809054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usgagas167123
 
8.9%
ustxtxs111362
 
5.9%
usncncs107424
 
5.7%
usflfls83024
 
4.4%
usscscs78977
 
4.2%
usnynyx75461
 
4.0%
usmsmss75117
 
4.0%
usalals64705
 
3.4%
usokoks29682
 
1.6%
usmnmns29432
 
1.6%
Other values (1630)1058158
56.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NWCG_REPORTING_UNIT_NAME
Categorical

HIGH CARDINALITY

Distinct1635
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
Georgia Forestry Commission
167123 
Texas A & M Forest Service
 
111362
North Carolina Forest Service
 
107424
Florida Forest Service
 
83024
South Carolina Forestry Commission
 
78977
Other values (1630)
1332555 

Length

Max length79
Median length27
Mean length27.3905651
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)< 0.1%

Sample

1st rowPlumas National Forest
2nd rowEldorado National Forest
3rd rowEldorado National Forest
4th rowEldorado National Forest
5th rowEldorado National Forest

Common Values

ValueCountFrequency (%)
Georgia Forestry Commission167123
 
8.9%
Texas A & M Forest Service111362
 
5.9%
North Carolina Forest Service107424
 
5.7%
Florida Forest Service83024
 
4.4%
South Carolina Forestry Commission78977
 
4.2%
Fire Department of New York75461
 
4.0%
Mississippi Forestry Commission75117
 
4.0%
Alabama Forestry Commission64705
 
3.4%
Oklahoma Division of Forestry29682
 
1.6%
Minnesota Department of Natural Resources29432
 
1.6%
Other values (1625)1058158
56.3%

Length

2022-03-08T09:44:37.961414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forestry656118
 
9.3%
forest584875
 
8.3%
commission414195
 
5.9%
of374462
 
5.3%
service367412
 
5.2%
national255791
 
3.6%
department205451
 
2.9%
carolina188995
 
2.7%
172978
 
2.4%
georgia167123
 
2.4%
Other values (1580)3687090
52.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOURCE_REPORTING_UNIT
Categorical

HIGH CARDINALITY

Distinct4992
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
GAGAS
 
97844
SCSCS
 
52064
TXTXS
 
40366
FLFLS
 
37945
NCNCS
 
37255
Other values (4987)
1614991 

Length

Max length21
Median length5
Mean length5.572214851
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique619 ?
Unique (%)< 0.1%

Sample

1st row0511
2nd row0503
3rd row0503
4th row0503
5th row0503

Common Values

ValueCountFrequency (%)
GAGAS97844
 
5.2%
SCSCS52064
 
2.8%
TXTXS40366
 
2.1%
FLFLS37945
 
2.0%
NCNCS37255
 
2.0%
TXVFD36266
 
1.9%
MSMSS31822
 
1.7%
MNMNS23914
 
1.3%
PRIITF21802
 
1.2%
WVDOF17088
 
0.9%
Other values (4982)1484099
78.9%

Length

2022-03-08T09:44:38.122269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gagas97844
 
4.9%
scscs52064
 
2.6%
ga51723
 
2.6%
txtxs40366
 
2.0%
flfls37945
 
1.9%
ncncs37255
 
1.9%
txvfd36266
 
1.8%
msmss31822
 
1.6%
ms28721
 
1.4%
mnmns23914
 
1.2%
Other values (4998)1563181
78.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOURCE_REPORTING_UNIT_NAME
Categorical

HIGH CARDINALITY

Distinct4441
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
Georgia Forestry Commission
 
97844
Fire Department of New York
 
75461
South Carolina Forestry Commission
 
52064
Mississippi Forestry Commission
 
46396
Texas Forest Service
 
42675
Other values (4436)
1566025 

Length

Max length74
Median length26
Mean length25.9990061
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique604 ?
Unique (%)< 0.1%

Sample

1st rowPlumas National Forest
2nd rowEldorado National Forest
3rd rowEldorado National Forest
4th rowEldorado National Forest
5th rowEldorado National Forest

Common Values

ValueCountFrequency (%)
Georgia Forestry Commission97844
 
5.2%
Fire Department of New York75461
 
4.0%
South Carolina Forestry Commission52064
 
2.8%
Mississippi Forestry Commission46396
 
2.5%
Texas Forest Service42675
 
2.3%
North Carolina Division of Forest Resources39879
 
2.1%
Florida Forest Service37945
 
2.0%
Minnesota Department of Natural Resources29432
 
1.6%
International Institute of Tropical Forestry21802
 
1.2%
Alabama Forestry Commission21133
 
1.1%
Other values (4431)1415834
75.3%

Length

2022-03-08T09:44:38.273587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forestry413969
 
5.9%
forest410529
 
5.8%
district331376
 
4.7%
of328446
 
4.7%
national250551
 
3.6%
commission228260
 
3.3%
department219146
 
3.1%
unit188114
 
2.7%
fire175238
 
2.5%
service158620
 
2.3%
Other values (3308)4316146
61.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LOCAL_FIRE_REPORT_ID
Categorical

HIGH CARDINALITY
MISSING

Distinct13508
Distinct (%)3.2%
Missing1459286
Missing (%)77.6%
Memory size14.3 MiB
001
 
8189
002
 
4960
1
 
3652
2
 
3554
3
 
3494
Other values (13503)
397330 

Length

Max length6
Median length2
Mean length2.501171711
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9939 ?
Unique (%)2.4%

Sample

1st row1
2nd row13
3rd row27
4th row43
5th row44

Common Values

ValueCountFrequency (%)
0018189
 
0.4%
0024960
 
0.3%
13652
 
0.2%
23554
 
0.2%
33494
 
0.2%
53433
 
0.2%
43423
 
0.2%
0033344
 
0.2%
63331
 
0.2%
73310
 
0.2%
Other values (13498)380489
 
20.2%
(Missing)1459286
77.6%

Length

2022-03-08T09:44:38.408352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0018189
 
1.9%
0024960
 
1.2%
13652
 
0.9%
23554
 
0.8%
33494
 
0.8%
53433
 
0.8%
43423
 
0.8%
0033344
 
0.8%
63331
 
0.8%
73310
 
0.8%
Other values (13497)380489
90.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LOCAL_INCIDENT_ID
Categorical

HIGH CARDINALITY
MISSING

Distinct565914
Distinct (%)53.4%
Missing820821
Missing (%)43.6%
Memory size14.3 MiB
001
 
3839
1
 
3258
2
 
2874
3
 
2647
002
 
2489
Other values (565909)
1044537 

Length

Max length28
Median length6
Mean length8.149549283
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique501595 ?
Unique (%)47.3%

Sample

1st rowPNF-47
2nd row13
3rd row021
4th row6
5th row7

Common Values

ValueCountFrequency (%)
0013839
 
0.2%
13258
 
0.2%
22874
 
0.2%
32647
 
0.1%
0022489
 
0.1%
42429
 
0.1%
52261
 
0.1%
102208
 
0.1%
62150
 
0.1%
112082
 
0.1%
Other values (565904)1033407
55.0%
(Missing)820821
43.6%

Length

2022-03-08T09:44:38.833339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0013848
 
0.4%
13275
 
0.3%
22892
 
0.3%
32664
 
0.3%
0022495
 
0.2%
42457
 
0.2%
52278
 
0.2%
102251
 
0.2%
62167
 
0.2%
112117
 
0.2%
Other values (551249)1036648
97.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FIRE_CODE
Categorical

HIGH CARDINALITY
MISSING

Distinct172446
Distinct (%)53.1%
Missing1555636
Missing (%)82.7%
Memory size14.3 MiB
D44Z
 
9451
5555
 
5144
D5GJ
 
3459
0001
 
3329
0000
 
1928
Other values (172441)
301518 

Length

Max length6
Median length4
Mean length3.997672622
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148058 ?
Unique (%)45.6%

Sample

1st rowBJ8K
2nd rowAAC0
3rd rowA32W
4th rowBK5X
5th rowBLPQ

Common Values

ValueCountFrequency (%)
D44Z9451
 
0.5%
55555144
 
0.3%
D5GJ3459
 
0.2%
00013329
 
0.2%
00001928
 
0.1%
23001892
 
0.1%
EKV31032
 
0.1%
47001003
 
0.1%
EKW0938
 
< 0.1%
0100904
 
< 0.1%
Other values (172436)295749
 
15.7%
(Missing)1555636
82.7%

Length

2022-03-08T09:44:38.965889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d44z9451
 
2.9%
55555144
 
1.6%
d5gj3459
 
1.1%
00013329
 
1.0%
00001928
 
0.6%
23001892
 
0.6%
ekv31032
 
0.3%
47001003
 
0.3%
ekw0938
 
0.3%
0100904
 
0.3%
Other values (172435)295675
91.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FIRE_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct493633
Distinct (%)53.5%
Missing957189
Missing (%)50.9%
Memory size14.3 MiB
GRASS FIRE
 
3983
NA
 
3214
UNKNOWN
 
3154
LOCAL
 
2068
STATE
 
1423
Other values (493628)
909434 

Length

Max length70
Median length10
Mean length11.52910181
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique414265 ?
Unique (%)44.9%

Sample

1st rowFOUNTAIN
2nd rowPIGEON
3rd rowSLACK
4th rowDEER
5th rowSTEVENOT

Common Values

ValueCountFrequency (%)
GRASS FIRE3983
 
0.2%
NA3214
 
0.2%
UNKNOWN3154
 
0.2%
LOCAL 2068
 
0.1%
STATE 1423
 
0.1%
LOCAL FIRE 726
 
< 0.1%
COTTONWOOD710
 
< 0.1%
POWERLINE660
 
< 0.1%
ROCK629
 
< 0.1%
LOCAL595
 
< 0.1%
Other values (493623)906114
48.2%
(Missing)957189
50.9%

Length

2022-03-08T09:44:39.153142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fire65498
 
4.1%
creek28232
 
1.8%
rd25146
 
1.6%
221218
 
1.3%
road19395
 
1.2%
19362
 
1.2%
112106
 
0.8%
lake9503
 
0.6%
hwy9468
 
0.6%
grass8964
 
0.6%
Other values (228903)1374069
86.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ICS_209_INCIDENT_NUMBER
Categorical

HIGH CARDINALITY
MISSING

Distinct22737
Distinct (%)88.4%
Missing1854748
Missing (%)98.6%
Memory size14.3 MiB
OK-OSA-100020
 
53
MT-BRF-000135
 
46
WA-OWF-000583
 
41
CA-MNF-000663
 
39
ID-PAF-006068
 
39
Other values (22732)
25499 

Length

Max length19
Median length13
Mean length12.08640199
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21712 ?
Unique (%)84.4%

Sample

1st rowCA-ENF-017646
2nd rowCA-ENF-18044
3rd rowNC-NCS-050201401
4th rowCA-LPF-1353
5th rowAZ-TNF-105

Common Values

ValueCountFrequency (%)
OK-OSA-10002053
 
< 0.1%
MT-BRF-00013546
 
< 0.1%
WA-OWF-00058341
 
< 0.1%
CA-MNF-00066339
 
< 0.1%
ID-PAF-00606839
 
< 0.1%
OR-UPF-00912137
 
< 0.1%
WA-OLP-082035
 
< 0.1%
OR-MHF-00001733
 
< 0.1%
WA-OWF-00055932
 
< 0.1%
ID-CWF-00001631
 
< 0.1%
Other values (22727)25331
 
1.3%
(Missing)1854748
98.6%

Length

2022-03-08T09:44:39.303700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ok-osa-10002053
 
0.2%
mt-brf-00013546
 
0.2%
wa-owf-00058341
 
0.2%
id-paf-00606839
 
0.2%
ca-mnf-00066339
 
0.2%
or-upf-00912137
 
0.1%
wa-olp-082035
 
0.1%
or-mhf-00001733
 
0.1%
wa-owf-00055932
 
0.1%
id-cwf-00001631
 
0.1%
Other values (22784)25463
98.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ICS_209_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct19573
Distinct (%)76.1%
Missing1854748
Missing (%)98.6%
Memory size14.3 MiB
OSAGE-MIAMI COMPLEX
 
53
Selway-Salmon WFU Complex
 
46
YAKIMA COMPLEX
 
41
South Fork Complex
 
39
Yolla Bolly Complex
 
39
Other values (19568)
25499 

Length

Max length37
Median length10
Mean length10.95466034
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17035 ?
Unique (%)66.2%

Sample

1st rowPOWER
2nd rowFREDS
3rd rowAustin Creek
4th rowCHIMINEAS
5th rowTHREE FIRE COMPLEX

Common Values

ValueCountFrequency (%)
OSAGE-MIAMI COMPLEX53
 
< 0.1%
Selway-Salmon WFU Complex46
 
< 0.1%
YAKIMA COMPLEX41
 
< 0.1%
South Fork Complex39
 
< 0.1%
Yolla Bolly Complex39
 
< 0.1%
Tiller Complex37
 
< 0.1%
Olympic Complex35
 
< 0.1%
Clackamas River Complex33
 
< 0.1%
Wenatchee Complex32
 
< 0.1%
Clear/Nez Complex31
 
< 0.1%
Other values (19563)25331
 
1.3%
(Missing)1854748
98.6%

Length

2022-03-08T09:44:39.455088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complex2972
 
6.2%
fire2335
 
4.9%
creek1906
 
4.0%
road798
 
1.7%
lake501
 
1.0%
2431
 
0.9%
mountain393
 
0.8%
fork363
 
0.8%
river337
 
0.7%
ridge335
 
0.7%
Other values (10811)37544
78.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MTBS_ID
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct10481
Distinct (%)95.3%
Missing1869462
Missing (%)99.4%
Memory size14.3 MiB
KY3686008359020011102
 
12
ID4542411459020120730
 
9
ID4568011472320070706
 
8
MT4574610716620120731
 
7
OR4493112043020110824
 
7
Other values (10476)
10960 

Length

Max length29
Median length21
Mean length21.0527129
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10136 ?
Unique (%)92.1%

Sample

1st rowCA3850212028020041006
2nd rowCA3878712031820041013
3rd rowFS-0417-018-20050715
4th rowAZ3372311127120050621
5th rowFS-0417-023-20050721

Common Values

ValueCountFrequency (%)
KY368600835902001110212
 
< 0.1%
ID45424114590201207309
 
< 0.1%
ID45680114723200707068
 
< 0.1%
MT45746107166201207317
 
< 0.1%
OR44931120430201108247
 
< 0.1%
UT39789112292199608026
 
< 0.1%
CA39852121444200808146
 
< 0.1%
NV40116117067200707166
 
< 0.1%
CA40546122663200806215
 
< 0.1%
ID44750115548200707175
 
< 0.1%
Other values (10471)10932
 
0.6%
(Missing)1869462
99.4%

Length

2022-03-08T09:44:39.588693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ky368600835902001110212
 
0.1%
id45424114590201207309
 
0.1%
id45680114723200707068
 
0.1%
mt45746107166201207317
 
0.1%
or44931120430201108247
 
0.1%
ut39789112292199608026
 
0.1%
ca39852121444200808146
 
0.1%
nv40116117067200707166
 
0.1%
tx32097101167201102275
 
< 0.1%
or43822119389200707065
 
< 0.1%
Other values (10471)10932
99.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MTBS_FIRE_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct8133
Distinct (%)73.9%
Missing1869462
Missing (%)99.4%
Memory size14.3 MiB
UNNAMED
 
752
COTTONWOOD
 
24
CANYON
 
14
WILLOW
 
12
BEAR
 
12
Other values (8128)
10189 

Length

Max length49
Median length9
Mean length10.56557303
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6975 ?
Unique (%)63.4%

Sample

1st rowPOWER
2nd rowFREDS
3rd rowSUMMIT
4th rowTHREE FIRE COMPLEX (THREE)
5th rowSLINKARD 2

Common Values

ValueCountFrequency (%)
UNNAMED752
 
< 0.1%
COTTONWOOD24
 
< 0.1%
CANYON14
 
< 0.1%
WILLOW12
 
< 0.1%
BEAR12
 
< 0.1%
BEAR CREEK12
 
< 0.1%
COYOTE12
 
< 0.1%
SHEEP11
 
< 0.1%
WILLOW CREEK11
 
< 0.1%
MUSTANG10
 
< 0.1%
Other values (8123)10133
 
0.5%
(Missing)1869462
99.4%

Length

2022-03-08T09:44:39.748806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complex944
 
5.0%
creek803
 
4.3%
unnamed752
 
4.0%
fire268
 
1.4%
2218
 
1.2%
lake197
 
1.0%
river182
 
1.0%
mountain164
 
0.9%
canyon163
 
0.9%
fork141
 
0.8%
Other values (6002)14950
79.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

COMPLEX_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct1416
Distinct (%)27.3%
Missing1875282
Missing (%)99.7%
Memory size14.3 MiB
OSAGE-MIAMI COMPLEX
 
54
TILLER COMPLEX
 
50
MOTORWAY COMPLEX
 
49
SELWAY-SALMON WFU COMPLEX
 
46
SOUTH FORK COMPLEX
 
42
Other values (1411)
4942 

Length

Max length43
Median length17
Mean length17.55315454
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique612 ?
Unique (%)11.8%

Sample

1st rowTHREE FIRE COMPLEX
2nd rowTHREE FIRE COMPLEX
3rd rowGOLDILOCKS COMPLEX
4th rowCARRIZO COMPLEX
5th rowCARRIZO COMPLEX

Common Values

ValueCountFrequency (%)
OSAGE-MIAMI COMPLEX54
 
< 0.1%
TILLER COMPLEX50
 
< 0.1%
MOTORWAY COMPLEX49
 
< 0.1%
SELWAY-SALMON WFU COMPLEX46
 
< 0.1%
SOUTH FORK COMPLEX42
 
< 0.1%
YAKIMA COMPLEX41
 
< 0.1%
YOLLA BOLLY COMPLEX 200839
 
< 0.1%
CLEAR/NEZ COMPLEX38
 
< 0.1%
VALLEY COMPLEX37
 
< 0.1%
OLYMPIC COMPLEX36
 
< 0.1%
Other values (1406)4751
 
0.3%
(Missing)1875282
99.7%

Length

2022-03-08T09:44:39.897033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complex5113
38.4%
creek175
 
1.3%
lightning170
 
1.3%
river159
 
1.2%
lake154
 
1.2%
wfu138
 
1.0%
fork133
 
1.0%
fire91
 
0.7%
south89
 
0.7%
red62
 
0.5%
Other values (1393)7039
52.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FIRE_YEAR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.709974
Minimum1992
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:40.011853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993
Q11998
median2004
Q32009
95-th percentile2014
Maximum2015
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.663098594
Coefficient of variation (CV)0.003325380758
Kurtosis-1.113110996
Mean2003.709974
Median Absolute Deviation (MAD)5
Skewness-0.05698186052
Sum3767906477
Variance44.39688288
MonotonicityNot monotonic
2022-03-08T09:44:40.121247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2006114004
 
6.1%
200096416
 
5.1%
200795573
 
5.1%
201190552
 
4.8%
199989363
 
4.8%
200588604
 
4.7%
200186587
 
4.6%
200885378
 
4.5%
201079889
 
4.2%
200978325
 
4.2%
Other values (14)975774
51.9%
ValueCountFrequency (%)
199267975
3.6%
199361989
3.3%
199475955
4.0%
199571472
3.8%
199675574
4.0%
199761450
3.3%
199868370
3.6%
199989363
4.8%
200096416
5.1%
200186587
4.6%
ValueCountFrequency (%)
201574491
4.0%
201467753
3.6%
201364780
3.4%
201272769
3.9%
201190552
4.8%
201079889
4.2%
200978325
4.2%
200885378
4.5%
200795573
5.1%
2006114004
6.1%

DISCOVERY_DATE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8766
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2453063.657
Minimum2448622.5
Maximum2457387.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:40.249985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2448622.5
5-th percentile2449148.5
Q12451084.5
median2453177.5
Q32455035.5
95-th percentile2456867.5
Maximum2457387.5
Range8765
Interquartile range (IQR)3951

Descriptive statistics

Standard deviation2434.573159
Coefficient of variation (CV)0.0009924622837
Kurtosis-1.105805095
Mean2453063.657
Median Absolute Deviation (MAD)1963
Skewness-0.05760334535
Sum4.61290035 × 1012
Variance5927146.467
MonotonicityNot monotonic
2022-03-08T09:44:40.404635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2454506.51208
 
0.1%
2453441.51177
 
0.1%
2453798.51121
 
0.1%
2455611.51108
 
0.1%
2449430.51070
 
0.1%
2453799.51052
 
0.1%
2453476.51001
 
0.1%
2456112.5958
 
0.1%
2454575.5936
 
< 0.1%
2449773.5933
 
< 0.1%
Other values (8756)1869901
99.4%
ValueCountFrequency (%)
2448622.5129
< 0.1%
2448623.546
 
< 0.1%
2448624.543
 
< 0.1%
2448625.568
< 0.1%
2448626.544
 
< 0.1%
2448627.592
< 0.1%
2448628.5152
< 0.1%
2448629.585
< 0.1%
2448630.535
 
< 0.1%
2448631.572
< 0.1%
ValueCountFrequency (%)
2457387.522
< 0.1%
2457386.517
 
< 0.1%
2457385.526
< 0.1%
2457384.520
< 0.1%
2457383.511
 
< 0.1%
2457382.533
< 0.1%
2457381.519
< 0.1%
2457380.537
< 0.1%
2457379.534
< 0.1%
2457378.546
< 0.1%

DISCOVERY_DOY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.719145
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:40.550424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q189
median164
Q3230
95-th percentile322
Maximum366
Range365
Interquartile range (IQR)141

Descriptive statistics

Standard deviation90.03890916
Coefficient of variation (CV)0.54662079
Kurtosis-0.8901164788
Mean164.719145
Median Absolute Deviation (MAD)71
Skewness0.2241694798
Sum309748587
Variance8107.005164
MonotonicityNot monotonic
2022-03-08T09:44:40.707013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18512875
 
0.7%
18611535
 
0.6%
1019261
 
0.5%
679260
 
0.5%
1089256
 
0.5%
1009248
 
0.5%
838949
 
0.5%
958924
 
0.5%
1098850
 
0.5%
1078787
 
0.5%
Other values (356)1783520
94.8%
ValueCountFrequency (%)
13971
0.2%
22719
0.1%
32506
0.1%
42449
0.1%
52703
0.1%
62571
0.1%
72874
0.2%
82908
0.2%
92394
0.1%
102354
0.1%
ValueCountFrequency (%)
366544
 
< 0.1%
3652733
0.1%
3642144
0.1%
3632176
0.1%
3622420
0.1%
3612514
0.1%
3601982
0.1%
3591290
0.1%
3581526
0.1%
3571794
0.1%

DISCOVERY_TIME
Real number (ℝ≥0)

MISSING

Distinct1440
Distinct (%)0.1%
Missing882638
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean1453.014326
Minimum0
Maximum2359
Zeros667
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:40.855071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile744
Q11240
median1457
Q31708
95-th percentile2101
Maximum2359
Range2359
Interquartile range (IQR)468

Descriptive statistics

Standard deviation405.9609626
Coefficient of variation (CV)0.2793922643
Kurtosis1.451234623
Mean1453.014326
Median Absolute Deviation (MAD)233
Skewness-0.6992740822
Sum1449856926
Variance164804.3031
MonotonicityNot monotonic
2022-03-08T09:44:40.989990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140020981
 
1.1%
150020020
 
1.1%
160018234
 
1.0%
130017280
 
0.9%
170014027
 
0.7%
120013963
 
0.7%
153012993
 
0.7%
143012901
 
0.7%
180012012
 
0.6%
163011554
 
0.6%
Other values (1430)843862
44.9%
(Missing)882638
46.9%
ValueCountFrequency (%)
0667
< 0.1%
1765
< 0.1%
2111
 
< 0.1%
398
 
< 0.1%
487
 
< 0.1%
5284
 
< 0.1%
674
 
< 0.1%
776
 
< 0.1%
891
 
< 0.1%
997
 
< 0.1%
ValueCountFrequency (%)
2359303
< 0.1%
2358109
 
< 0.1%
2357108
 
< 0.1%
2356108
 
< 0.1%
2355206
< 0.1%
2354108
 
< 0.1%
235393
 
< 0.1%
2352105
 
< 0.1%
235181
 
< 0.1%
2350307
< 0.1%

STAT_CAUSE_CODE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9790371
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:41.102189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.483860199
Coefficient of variation (CV)0.5826791406
Kurtosis-0.6013728157
Mean5.9790371
Median Absolute Deviation (MAD)3
Skewness0.3115850498
Sum11243370
Variance12.13728188
MonotonicityNot monotonic
2022-03-08T09:44:41.198500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5429028
22.8%
9323805
17.2%
7281455
15.0%
1278468
14.8%
13166723
 
8.9%
2147612
 
7.8%
476139
 
4.0%
861167
 
3.3%
352869
 
2.8%
633455
 
1.8%
Other values (3)29744
 
1.6%
ValueCountFrequency (%)
1278468
14.8%
2147612
 
7.8%
352869
 
2.8%
476139
 
4.0%
5429028
22.8%
633455
 
1.8%
7281455
15.0%
861167
 
3.3%
9323805
17.2%
1011500
 
0.6%
ValueCountFrequency (%)
13166723
 
8.9%
123796
 
0.2%
1114448
 
0.8%
1011500
 
0.6%
9323805
17.2%
861167
 
3.3%
7281455
15.0%
633455
 
1.8%
5429028
22.8%
476139
 
4.0%

STAT_CAUSE_DESCR
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
Debris Burning
429028 
Miscellaneous
323805 
Arson
281455 
Lightning
278468 
Missing/Undefined
166723 
Other values (8)
400986 

Length

Max length17
Median length13
Mean length11.10707086
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiscellaneous
2nd rowLightning
3rd rowDebris Burning
4th rowLightning
5th rowLightning

Common Values

ValueCountFrequency (%)
Debris Burning429028
22.8%
Miscellaneous323805
17.2%
Arson281455
15.0%
Lightning278468
14.8%
Missing/Undefined166723
 
8.9%
Equipment Use147612
 
7.8%
Campfire76139
 
4.0%
Children61167
 
3.3%
Smoking52869
 
2.8%
Railroad33455
 
1.8%
Other values (3)29744
 
1.6%

Length

2022-03-08T09:44:41.302957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debris429028
17.5%
burning429028
17.5%
miscellaneous323805
13.2%
arson281455
11.5%
lightning278468
11.3%
missing/undefined166723
 
6.8%
equipment147612
 
6.0%
use147612
 
6.0%
campfire76139
 
3.1%
children61167
 
2.5%
Other values (5)116068
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CONT_DATE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8760
Distinct (%)0.9%
Missing891531
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean2453237.753
Minimum2448622.5
Maximum2457391.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:41.442489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2448622.5
5-th percentile2449059.5
Q12450700.75
median2453466.5
Q32455753.5
95-th percentile2457106.5
Maximum2457391.5
Range8769
Interquartile range (IQR)5052.75

Descriptive statistics

Standard deviation2687.547698
Coefficient of variation (CV)0.001095510492
Kurtosis-1.324652733
Mean2453237.753
Median Absolute Deviation (MAD)2419
Skewness-0.1209844344
Sum2.426090224 × 1012
Variance7222912.628
MonotonicityNot monotonic
2022-03-08T09:44:41.753937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2455611.5841
 
< 0.1%
2457067.5668
 
< 0.1%
2449056.5649
 
< 0.1%
2448683.5631
 
< 0.1%
2450137.5631
 
< 0.1%
2456367.5631
 
< 0.1%
2449430.5605
 
< 0.1%
2455606.5594
 
< 0.1%
2450309.5593
 
< 0.1%
2450138.5588
 
< 0.1%
Other values (8750)982503
52.2%
(Missing)891531
47.4%
ValueCountFrequency (%)
2448622.570
< 0.1%
2448623.522
 
< 0.1%
2448624.523
 
< 0.1%
2448625.544
< 0.1%
2448626.529
 
< 0.1%
2448627.567
< 0.1%
2448628.5103
< 0.1%
2448629.545
< 0.1%
2448630.516
 
< 0.1%
2448631.541
 
< 0.1%
ValueCountFrequency (%)
2457391.51
 
< 0.1%
2457388.51
 
< 0.1%
2457387.520
< 0.1%
2457386.515
< 0.1%
2457385.524
< 0.1%
2457384.521
< 0.1%
2457383.510
 
< 0.1%
2457382.518
< 0.1%
2457381.519
< 0.1%
2457380.526
< 0.1%

CONT_DOY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct366
Distinct (%)< 0.1%
Missing891531
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean172.6567658
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:41.899964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile39
Q1102
median181
Q3232
95-th percentile316
Maximum366
Range365
Interquartile range (IQR)130

Descriptive statistics

Standard deviation84.32034771
Coefficient of variation (CV)0.4883697858
Kurtosis-0.8031035961
Mean172.6567658
Median Absolute Deviation (MAD)65
Skewness0.06155756373
Sum170746146
Variance7109.921038
MonotonicityNot monotonic
2022-03-08T09:44:42.042279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1857325
 
0.4%
1867091
 
0.4%
1845389
 
0.3%
1875268
 
0.3%
2045246
 
0.3%
2055215
 
0.3%
2165181
 
0.3%
2145181
 
0.3%
2065146
 
0.3%
2155143
 
0.3%
Other values (356)932749
49.6%
(Missing)891531
47.4%
ValueCountFrequency (%)
11088
0.1%
2805
< 0.1%
3803
< 0.1%
4899
< 0.1%
5956
0.1%
6963
0.1%
71022
0.1%
8985
0.1%
9959
0.1%
10891
< 0.1%
ValueCountFrequency (%)
366135
 
< 0.1%
365778
< 0.1%
364672
< 0.1%
363730
< 0.1%
362877
< 0.1%
361842
< 0.1%
360737
< 0.1%
359523
< 0.1%
358552
< 0.1%
357687
< 0.1%

CONT_TIME
Categorical

HIGH CARDINALITY
MISSING

Distinct1441
Distinct (%)0.2%
Missing972173
Missing (%)51.7%
Memory size14.3 MiB
1800
 
38078
1600
 
22167
1700
 
20606
1200
 
19276
1500
 
18757
Other values (1436)
789408 

Length

Max length4
Median length4
Mean length3.998326529
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1730
2nd row1530
3rd row2024
4th row1400
5th row1200

Common Values

ValueCountFrequency (%)
180038078
 
2.0%
160022167
 
1.2%
170020606
 
1.1%
120019276
 
1.0%
150018757
 
1.0%
200017315
 
0.9%
140016234
 
0.9%
190015354
 
0.8%
163014367
 
0.8%
130012963
 
0.7%
Other values (1431)713175
37.9%
(Missing)972173
51.7%

Length

2022-03-08T09:44:42.181174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
180038078
 
4.2%
160022167
 
2.4%
170020606
 
2.3%
120019276
 
2.1%
150018757
 
2.1%
200017315
 
1.9%
140016234
 
1.8%
190015354
 
1.7%
163014367
 
1.6%
130012963
 
1.4%
Other values (1430)712795
78.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FIRE_SIZE
Real number (ℝ≥0)

SKEWED

Distinct13637
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.52015834
Minimum1 × 10-5
Maximum606945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:42.315944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.1
Q10.1
median1
Q33.3
95-th percentile45
Maximum606945
Range606945
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2497.59818
Coefficient of variation (CV)33.51573904
Kurtosis16159.39785
Mean74.52015834
Median Absolute Deviation (MAD)0.9
Skewness106.83733
Sum140132549.6
Variance6237996.668
MonotonicityNot monotonic
2022-03-08T09:44:42.469807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1459145
24.4%
1221680
 
11.8%
0.5113222
 
6.0%
2109493
 
5.8%
0.275302
 
4.0%
365784
 
3.5%
561963
 
3.3%
0.2554325
 
2.9%
0.352973
 
2.8%
438191
 
2.0%
Other values (13627)628387
33.4%
ValueCountFrequency (%)
1 × 10-51
 
< 0.1%
9 × 10-51
 
< 0.1%
0.000113
< 0.1%
0.00024
 
< 0.1%
0.000221
 
< 0.1%
0.000342
 
< 0.1%
0.00041
 
< 0.1%
0.0004591
 
< 0.1%
0.00081
 
< 0.1%
0.00091
 
< 0.1%
ValueCountFrequency (%)
6069451
< 0.1%
558198.31
< 0.1%
5380491
< 0.1%
5376271
< 0.1%
5170781
< 0.1%
4999451
< 0.1%
4832801
< 0.1%
4795491
< 0.1%
4639941
< 0.1%
4610471
< 0.1%

FIRE_SIZE_CLASS
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
B
939376 
A
666919 
C
220077 
D
 
28427
E
 
14107
Other values (2)
 
11559

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B939376
50.0%
A666919
35.5%
C220077
 
11.7%
D28427
 
1.5%
E14107
 
0.8%
F7786
 
0.4%
G3773
 
0.2%

Length

2022-03-08T09:44:42.600067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-08T09:44:42.690749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
b939376
50.0%
a666919
35.5%
c220077
 
11.7%
d28427
 
1.5%
e14107
 
0.8%
f7786
 
0.4%
g3773
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LATITUDE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct894061
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.78121281
Minimum17.93972222
Maximum70.3306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:42.802335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17.93972222
5-th percentile29.21543111
Q132.8186
median35.4525
Q340.8272
95-th percentile47.204088
Maximum70.3306
Range52.39087778
Interquartile range (IQR)8.0086

Descriptive statistics

Standard deviation6.139031271
Coefficient of variation (CV)0.1669067114
Kurtosis1.910779023
Mean36.78121281
Median Absolute Deviation (MAD)3.54222
Skewness0.4883635083
Sum69165783.34
Variance37.68770494
MonotonicityNot monotonic
2022-03-08T09:44:42.949048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.8666955
 
0.1%
33.3353706
 
< 0.1%
33.3517605
 
< 0.1%
47.8833585
 
< 0.1%
17.970539571
 
< 0.1%
35.3526
 
< 0.1%
33.925517
 
< 0.1%
35.6833496
 
< 0.1%
41.0665495
 
< 0.1%
33.3167471
 
< 0.1%
Other values (894051)1874538
99.7%
ValueCountFrequency (%)
17.939722221
 
< 0.1%
17.9449241
 
< 0.1%
17.951
 
< 0.1%
17.9513891
 
< 0.1%
17.951941
 
< 0.1%
17.9538891
 
< 0.1%
17.955277781
 
< 0.1%
17.956533164
< 0.1%
17.9566671
 
< 0.1%
17.9571411
 
< 0.1%
ValueCountFrequency (%)
70.33061
< 0.1%
70.13811
< 0.1%
70.13781
< 0.1%
69.84951
< 0.1%
69.78281
< 0.1%
69.777451
< 0.1%
69.61891
< 0.1%
69.46811
< 0.1%
69.45251
< 0.1%
69.4331
< 0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION

Distinct997536
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-95.70494159
Minimum-178.8026
Maximum-65.25694444
Zeros0
Zeros (%)0.0%
Negative1880465
Negative (%)100.0%
Memory size14.3 MiB
2022-03-08T09:44:43.121169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-178.8026
5-th percentile-122.035
Q1-110.36347
median-92.043043
Q3-82.2976
95-th percentile-74.2012
Maximum-65.25694444
Range113.5456556
Interquartile range (IQR)28.06587

Descriptive statistics

Standard deviation16.71694396
Coefficient of variation (CV)-0.1746716908
Kurtosis0.1398536113
Mean-95.70494159
Median Absolute Deviation (MAD)10.981916
Skewness-0.7172878416
Sum-179969793
Variance279.4562154
MonotonicityNot monotonic
2022-03-08T09:44:43.276541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-110.4518792
 
< 0.1%
-123.6845745
 
< 0.1%
-66.246414571
 
< 0.1%
-110.4507507
 
< 0.1%
-66.386131384
 
< 0.1%
-123.6678371
 
< 0.1%
-81.9358
 
< 0.1%
-95.0169356
 
< 0.1%
-79.75355
 
< 0.1%
-66.114357352
 
< 0.1%
Other values (997526)1875674
99.7%
ValueCountFrequency (%)
-178.80261
< 0.1%
-173.38571
< 0.1%
-170.36941
< 0.1%
-168.871
< 0.1%
-166.86941
< 0.1%
-166.26931
< 0.1%
-166.16671
< 0.1%
-166.15271
< 0.1%
-166.151
< 0.1%
-166.0531
< 0.1%
ValueCountFrequency (%)
-65.256944441
 
< 0.1%
-65.2641756
< 0.1%
-65.270277781
 
< 0.1%
-65.273611111
 
< 0.1%
-65.274444441
 
< 0.1%
-65.2748953
< 0.1%
-65.275555561
 
< 0.1%
-65.278888891
 
< 0.1%
-65.285833333
< 0.1%
-65.28751
 
< 0.1%

OWNER_CODE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.59657797
Minimum0
Maximum15
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:43.417124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median14
Q314
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.404662212
Coefficient of variation (CV)0.4156683622
Kurtosis-0.8057555661
Mean10.59657797
Median Absolute Deviation (MAD)0
Skewness-0.827545277
Sum19926494
Variance19.4010492
MonotonicityNot monotonic
2022-03-08T09:44:43.515070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
141050835
55.9%
8314822
 
16.7%
5188338
 
10.0%
2106819
 
5.7%
1371881
 
3.8%
163278
 
3.4%
730790
 
1.6%
317524
 
0.9%
412191
 
0.6%
98952
 
0.5%
Other values (6)15035
 
0.8%
ValueCountFrequency (%)
015
 
< 0.1%
163278
 
3.4%
2106819
 
5.7%
317524
 
0.9%
412191
 
0.6%
5188338
10.0%
66452
 
0.3%
730790
 
1.6%
8314822
16.7%
98952
 
0.5%
ValueCountFrequency (%)
152206
 
0.1%
141050835
55.9%
1371881
 
3.8%
124236
 
0.2%
111841
 
0.1%
10285
 
< 0.1%
98952
 
0.5%
8314822
 
16.7%
730790
 
1.6%
66452
 
0.3%

OWNER_DESCR
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
MISSING/NOT SPECIFIED
1050835 
PRIVATE
314822 
USFS
188338 
BIA
106819 
STATE OR PRIVATE
 
71881
Other values (11)
147770 

Length

Max length21
Median length21
Mean length14.45321184
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSFS
2nd rowUSFS
3rd rowSTATE OR PRIVATE
4th rowUSFS
5th rowUSFS

Common Values

ValueCountFrequency (%)
MISSING/NOT SPECIFIED1050835
55.9%
PRIVATE314822
 
16.7%
USFS188338
 
10.0%
BIA106819
 
5.7%
STATE OR PRIVATE71881
 
3.8%
BLM63278
 
3.4%
STATE30790
 
1.6%
NPS17524
 
0.9%
FWS12191
 
0.6%
TRIBAL8952
 
0.5%
Other values (6)15035
 
0.8%

Length

2022-03-08T09:44:43.635711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing/not1050835
34.1%
specified1050835
34.1%
private386703
 
12.5%
usfs188338
 
6.1%
bia106819
 
3.5%
state102671
 
3.3%
or71881
 
2.3%
blm63278
 
2.1%
nps17524
 
0.6%
fws12191
 
0.4%
Other values (8)32645
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

STATE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.3 MiB
CA
189550 
GA
168867 
TX
142021 
NC
 
111277
FL
 
90261
Other values (47)
1178489 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA189550
 
10.1%
GA168867
 
9.0%
TX142021
 
7.6%
NC111277
 
5.9%
FL90261
 
4.8%
SC81315
 
4.3%
NY80870
 
4.3%
MS79230
 
4.2%
AZ71586
 
3.8%
AL66570
 
3.5%
Other values (42)798918
42.5%

Length

2022-03-08T09:44:43.764882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca189550
 
10.1%
ga168867
 
9.0%
tx142021
 
7.6%
nc111277
 
5.9%
fl90261
 
4.8%
sc81315
 
4.3%
ny80870
 
4.3%
ms79230
 
4.2%
az71586
 
3.8%
al66570
 
3.5%
Other values (42)798918
42.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

COUNTY
Categorical

HIGH CARDINALITY
MISSING

Distinct3455
Distinct (%)0.3%
Missing678148
Missing (%)36.1%
Memory size14.3 MiB
5
 
7576
Lincoln
 
7405
SUFFOLK
 
7373
Polk
 
6955
Washington
 
6916
Other values (3450)
1166092 

Length

Max length50
Median length7
Mean length7.130708457
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique392 ?
Unique (%)< 0.1%

Sample

1st row63
2nd row61
3rd row17
4th row3
5th row3

Common Values

ValueCountFrequency (%)
57576
 
0.4%
Lincoln7405
 
0.4%
SUFFOLK7373
 
0.4%
Polk6955
 
0.4%
Washington6916
 
0.4%
Cherokee6796
 
0.4%
Oahu6777
 
0.4%
Marion6657
 
0.4%
Jackson6305
 
0.3%
Lee5914
 
0.3%
Other values (3445)1133643
60.3%
(Missing)678148
36.1%

Length

2022-03-08T09:44:43.883162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county23391
 
1.8%
san10339
 
0.8%
st10124
 
0.8%
washington10079
 
0.8%
lincoln8456
 
0.7%
jefferson8353
 
0.6%
marion7834
 
0.6%
polk7759
 
0.6%
cherokee7714
 
0.6%
suffolk7646
 
0.6%
Other values (2162)1196551
92.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FIPS_CODE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct285
Distinct (%)< 0.1%
Missing678148
Missing (%)36.1%
Infinite0
Infinite (%)0.0%
Mean95.78349969
Minimum1
Maximum810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 MiB
2022-03-08T09:44:44.016309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q129
median67
Q3121
95-th percentile313
Maximum810
Range809
Interquartile range (IQR)92

Descriptive statistics

Standard deviation98.61505178
Coefficient of variation (CV)1.029562003
Kurtosis3.908476849
Mean95.78349969
Median Absolute Deviation (MAD)42
Skewness1.925694026
Sum115162130
Variance9724.928437
MonotonicityNot monotonic
2022-03-08T09:44:44.160153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
529069
 
1.5%
328850
 
1.5%
2928519
 
1.5%
127042
 
1.4%
722230
 
1.2%
1921909
 
1.2%
1721776
 
1.2%
1521321
 
1.1%
3520710
 
1.1%
2119411
 
1.0%
Other values (275)961480
51.1%
(Missing)678148
36.1%
ValueCountFrequency (%)
127042
1.4%
328850
1.5%
529069
1.5%
6383
 
< 0.1%
722230
1.2%
915847
0.8%
1113832
0.7%
12145
 
< 0.1%
1317646
0.9%
1521321
1.1%
ValueCountFrequency (%)
81019
 
< 0.1%
80049
 
< 0.1%
7601
 
< 0.1%
7302
 
< 0.1%
7002
 
< 0.1%
6501
 
< 0.1%
5701
 
< 0.1%
55014
 
< 0.1%
5301
 
< 0.1%
510180
< 0.1%

FIPS_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct1698
Distinct (%)0.1%
Missing678148
Missing (%)36.1%
Memory size14.3 MiB
Washington
 
11014
Lincoln
 
10571
Jackson
 
9902
Marion
 
8908
Cherokee
 
8558
Other values (1693)
1153364 

Length

Max length31
Median length7
Mean length6.994575474
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowPlumas
2nd rowPlacer
3rd rowEl Dorado
4th rowAlpine
5th rowAlpine

Common Values

ValueCountFrequency (%)
Washington11014
 
0.6%
Lincoln10571
 
0.6%
Jackson9902
 
0.5%
Marion8908
 
0.5%
Cherokee8558
 
0.5%
Polk8300
 
0.4%
Monroe8173
 
0.4%
Coconino7900
 
0.4%
Jefferson7770
 
0.4%
Suffolk7597
 
0.4%
Other values (1688)1113624
59.2%
(Missing)678148
36.1%

Length

2022-03-08T09:44:44.305610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san12654
 
1.0%
washington11014
 
0.9%
st10976
 
0.9%
lincoln10571
 
0.8%
jackson9902
 
0.8%
jefferson9522
 
0.7%
marion8908
 
0.7%
cherokee8558
 
0.7%
polk8300
 
0.6%
monroe8173
 
0.6%
Other values (1724)1185193
92.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shape
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size14.3 MiB

Interactions

2022-03-08T09:42:54.051596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:12.473473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:33.391478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:53.651627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:14.142493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:34.480245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:55.933145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:31.525350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:51.638102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:11.295430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:30.026201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:50.438126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:11.468614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:32.664475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:56.110376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:12.973281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:33.878350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:54.140233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:14.599581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:34.978587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:01.705832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:31.998813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:51.972946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:11.622537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:30.491277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:50.915939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:11.958518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:33.141113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:58.164080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:13.428716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:34.342979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:54.584316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:15.052024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:35.456112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:07.553200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:32.469643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:52.304733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:11.948988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:30.949729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:51.383687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:12.441591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:33.615415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:00.404793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:13.887864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:34.846943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:55.033972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:15.492186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:35.945240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:13.510614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:32.939159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:52.618993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:12.267183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:31.414672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:51.855443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:12.920821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:34.092144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:02.481508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:14.341125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:35.339497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:55.480525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:15.948560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:36.403696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:19.731328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:33.404548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:52.928585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:12.585426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:31.876934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:52.320619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:13.397683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:34.560474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:04.823466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:15.617651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:36.465447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:56.572166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:17.016701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:37.665390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:26.093065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:34.531785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:53.989706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:13.712281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:32.969904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:53.395696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:14.935056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:35.877958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:17.415862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:27.119684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:47.177196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:07.923232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:27.931751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:48.821057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:42.704687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:45.567126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:05.820517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:24.623299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:44.008407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:05.059597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:26.233829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:46.960690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:19.096021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:27.448982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:47.503655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:08.197676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:28.202026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:49.109937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:48.260911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:45.855243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:06.154878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:24.941417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:44.297345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:05.346911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:26.524681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:47.246490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:20.731178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:27.751529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:47.797735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:08.695474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:28.503939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:49.424693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:39:54.106910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:46.165488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:06.785840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:25.256103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:44.599111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:05.660336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:26.826510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:47.728664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:22.723229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:28.231576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:48.272794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:09.186877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:28.995589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:49.937252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:00.102359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:46.676378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:07.102122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:25.568154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:45.077836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:06.171629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:27.329735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:48.233969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:24.761273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:28.742618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:48.780049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:09.638373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:29.454983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:50.412255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:06.104596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:47.152142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:07.416876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:25.889309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:45.552487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:06.628851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:27.820368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:48.703412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:26.791298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:29.253287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:49.288805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:10.085127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:29.928419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:50.880383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:12.054790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:47.624644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:07.725866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:26.206474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:46.013434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:07.102561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:28.279294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:49.171041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:29.082844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:29.761673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:49.800103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:10.535499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:30.426293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:51.350585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:17.858248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:48.100659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:08.029290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:26.522650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:46.483046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:07.574172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:28.763087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:49.630266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:43:31.608694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:30.991070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:37:51.112736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:11.736755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:31.850708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:38:52.659750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:24.415527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:40:49.366118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:09.076975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:27.705637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:41:47.744658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:08.962675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:30.059457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:42:50.915703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-08T09:44:44.576270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:44:44.768018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:44:44.940796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:44:45.109313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-08T09:44:45.270353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2022-03-08T09:43:41.038229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-08T09:43:52.193718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-08T09:44:14.717503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-08T09:44:23.331688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

OBJECTIDFOD_IDFPA_IDSOURCE_SYSTEM_TYPESOURCE_SYSTEMNWCG_REPORTING_AGENCYNWCG_REPORTING_UNIT_IDNWCG_REPORTING_UNIT_NAMESOURCE_REPORTING_UNITSOURCE_REPORTING_UNIT_NAMELOCAL_FIRE_REPORT_IDLOCAL_INCIDENT_IDFIRE_CODEFIRE_NAMEICS_209_INCIDENT_NUMBERICS_209_NAMEMTBS_IDMTBS_FIRE_NAMECOMPLEX_NAMEFIRE_YEARDISCOVERY_DATEDISCOVERY_DOYDISCOVERY_TIMESTAT_CAUSE_CODESTAT_CAUSE_DESCRCONT_DATECONT_DOYCONT_TIMEFIRE_SIZEFIRE_SIZE_CLASSLATITUDELONGITUDEOWNER_CODEOWNER_DESCRSTATECOUNTYFIPS_CODEFIPS_NAMEShape
011FS-1418826FEDFS-FIRESTATFSUSCAPNFPlumas National Forest0511Plumas National Forest1PNF-47BJ8KFOUNTAINNoneNoneNoneNoneNone20052453403.53313009.0Miscellaneous2453403.533.017300.10A40.036944-121.0058335.0USFSCA63063Plumasb'\x00\x01\xad\x10\x00\x00\xe8d\xc2\x92_@^\xc0\xe0\xc8l\x98\xba\x04D@\xe8d\xc2\x92_@^\xc0\xe0\xc8l\x98\xba\x04D@|\x01\x00\x00\x00\xe8d\xc2\x92_@^\xc0\xe0\xc8l\x98\xba\x04D@\xfe'
122FS-1418827FEDFS-FIRESTATFSUSCAENFEldorado National Forest0503Eldorado National Forest1313AAC0PIGEONNoneNoneNoneNoneNone20042453137.513308451.0Lightning2453137.5133.015300.25A38.933056-120.4044445.0USFSCA61061Placerb'\x00\x01\xad\x10\x00\x00T\xb6\xeej\xe2\x19^\xc0\x90\xc6U]nwC@T\xb6\xeej\xe2\x19^\xc0\x90\xc6U]nwC@|\x01\x00\x00\x00T\xb6\xeej\xe2\x19^\xc0\x90\xc6U]nwC@\xfe'
233FS-1418835FEDFS-FIRESTATFSUSCAENFEldorado National Forest0503Eldorado National Forest27021A32WSLACKNoneNoneNoneNoneNone20042453156.515219215.0Debris Burning2453156.5152.020240.10A38.984167-120.73555613.0STATE OR PRIVATECA17017El Doradob'\x00\x01\xad\x10\x00\x00\xd0\xa5\xa0W\x13/^\xc0P\xbbf,\xf9}C@\xd0\xa5\xa0W\x13/^\xc0P\xbbf,\xf9}C@|\x01\x00\x00\x00\xd0\xa5\xa0W\x13/^\xc0P\xbbf,\xf9}C@\xfe'
344FS-1418845FEDFS-FIRESTATFSUSCAENFEldorado National Forest0503Eldorado National Forest436NoneDEERNoneNoneNoneNoneNone20042453184.518016001.0Lightning2453189.5185.014000.10A38.559167-119.9133335.0USFSCA3003Alpineb'\x00\x01\xad\x10\x00\x00\x94\xac\xa3\rt\xfa]\xc0\xe8T\x00\xc6\x92GC@\x94\xac\xa3\rt\xfa]\xc0\xe8T\x00\xc6\x92GC@|\x01\x00\x00\x00\x94\xac\xa3\rt\xfa]\xc0\xe8T\x00\xc6\x92GC@\xfe'
455FS-1418847FEDFS-FIRESTATFSUSCAENFEldorado National Forest0503Eldorado National Forest447NoneSTEVENOTNoneNoneNoneNoneNone20042453184.518016001.0Lightning2453189.5185.012000.10A38.559167-119.9330565.0USFSCA3003Alpineb'\x00\x01\xad\x10\x00\x00@\xe3\xaa.\xb7\xfb]\xc0\xe8T\x00\xc6\x92GC@@\xe3\xaa.\xb7\xfb]\xc0\xe8T\x00\xc6\x92GC@|\x01\x00\x00\x00@\xe3\xaa.\xb7\xfb]\xc0\xe8T\x00\xc6\x92GC@\xfe'
566FS-1418849FEDFS-FIRESTATFSUSCAENFEldorado National Forest0503Eldorado National Forest548NoneHIDDENNoneNoneNoneNoneNone20042453186.518218001.0Lightning2453187.5183.016000.10A38.635278-120.1036115.0USFSCA5005Amadorb'\x00\x01\xad\x10\x00\x00\xf0<~\x90\xa1\x06^\xc0\xe0|D\xc8PQC@\xf0<~\x90\xa1\x06^\xc0\xe0|D\xc8PQC@|\x01\x00\x00\x00\xf0<~\x90\xa1\x06^\xc0\xe0|D\xc8PQC@\xfe'
677FS-1418851FEDFS-FIRESTATFSUSCAENFEldorado National Forest0503Eldorado National Forest589NoneFORKNoneNoneNoneNoneNone20042453187.518318001.0Lightning2453188.5184.014000.10A38.688333-120.1533335.0USFSCA17017El Doradob'\x00\x01\xad\x10\x00\x00$o\x996\xd0\t^\xc0h\x8czN\x1bXC@$o\x996\xd0\t^\xc0h\x8czN\x1bXC@|\x01\x00\x00\x00$o\x996\xd0\t^\xc0h\x8czN\x1bXC@\xfe'
788FS-1418854FEDFS-FIRESTATFSUSCASHFShasta-Trinity National Forest0514Shasta-Trinity National Forest302BK5XSLATENoneNoneNoneNoneNone20052453437.56713005.0Debris Burning2453437.567.016000.80B40.968056-122.43388913.0STATE OR PRIVATECANoneNoneNoneb'\x00\x01\xad\x10\x00\x00t)\xe8\xd5\xc4\x9b^\xc0\xa0t\x9d>\xe9{D@t)\xe8\xd5\xc4\x9b^\xc0\xa0t\x9d>\xe9{D@|\x01\x00\x00\x00t)\xe8\xd5\xc4\x9b^\xc0\xa0t\x9d>\xe9{D@\xfe'
899FS-1418856FEDFS-FIRESTATFSUSCASHFShasta-Trinity National Forest0514Shasta-Trinity National Forest503BLPQSHASTANoneNoneNoneNoneNone20052453444.57412005.0Debris Burning2453444.574.017001.00B41.233611-122.28333313.0STATE OR PRIVATECANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xdc\x8d\x1e""\x92^\xc0X\xb7\x06\xf8\xe6\x9dD@\xdc\x8d\x1e""\x92^\xc0X\xb7\x06\xf8\xe6\x9dD@|\x01\x00\x00\x00\xdc\x8d\x1e""\x92^\xc0X\xb7\x06\xf8\xe6\x9dD@\xfe'
91010FS-1418859FEDFS-FIRESTATFSUSCAENFEldorado National Forest0503Eldorado National Forest6110NoneTANGLEFOOTNoneNoneNoneNoneNone20042453187.518318001.0Lightning2453188.5184.018000.10A38.548333-120.1491675.0USFSCA5005Amadorb'\x00\x01\xad\x10\x00\x00dS\\\xf2\x8b\t^\xc0\x18\xd4[\xc9/FC@dS\\\xf2\x8b\t^\xc0\x18\xd4[\xc9/FC@|\x01\x00\x00\x00dS\\\xf2\x8b\t^\xc0\x18\xd4[\xc9/FC@\xfe'

Last rows

OBJECTIDFOD_IDFPA_IDSOURCE_SYSTEM_TYPESOURCE_SYSTEMNWCG_REPORTING_AGENCYNWCG_REPORTING_UNIT_IDNWCG_REPORTING_UNIT_NAMESOURCE_REPORTING_UNITSOURCE_REPORTING_UNIT_NAMELOCAL_FIRE_REPORT_IDLOCAL_INCIDENT_IDFIRE_CODEFIRE_NAMEICS_209_INCIDENT_NUMBERICS_209_NAMEMTBS_IDMTBS_FIRE_NAMECOMPLEX_NAMEFIRE_YEARDISCOVERY_DATEDISCOVERY_DOYDISCOVERY_TIMESTAT_CAUSE_CODESTAT_CAUSE_DESCRCONT_DATECONT_DOYCONT_TIMEFIRE_SIZEFIRE_SIZE_CLASSLATITUDELONGITUDEOWNER_CODEOWNER_DESCRSTATECOUNTYFIPS_CODEFIPS_NAMEShape
188045518804563003483112015CAIRS27458827NONFEDST-CACDFST/C&LUSCATCUTuolumne-Calaveras UnitCATCUTuolumne-Calaveras Unit592668006603NoneCOVENoneNoneNoneNoneNone20152457201.517920429.0Miscellaneous2457202.5180.000005.30B37.936253-120.61374313.0STATE OR PRIVATECANoneNoneNoneb"\x00\x01\xad\x10\x00\x00\x84I\xb8\x90G'^\xc0 \xe4g#\xd7\xf7B@\x84I\xb8\x90G'^\xc0 \xe4g#\xd7\xf7B@|\x01\x00\x00\x00\x84I\xb8\x90G'^\xc0 \xe4g#\xd7\xf7B@\xfe"
188045618804573003483282015CAIRS27369138NONFEDST-CACDFST/C&LUSCATGUTehama-Glenn UnitCATGUTehama-Glenn Unit580277005039NoneRANCHO 6NoneNoneNoneNoneNone20152457187.5165171413.0Missing/Undefined2457187.5165.019132.22B40.019907-122.39139813.0STATE OR PRIVATECANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\x10n2\xaa\x0c\x99^\xc0\x18\xfb\x04P\x8c\x02D@\x10n2\xaa\x0c\x99^\xc0\x18\xfb\x04P\x8c\x02D@|\x01\x00\x00\x00\x10n2\xaa\x0c\x99^\xc0\x18\xfb\x04P\x8c\x02D@\xfe'
188045718804583003483542015CAIRS28234594NONFEDST-CACDFST/C&LUSCASHUShasta-Trinity UnitCASHUShasta-Trinity Unit591111009503NoneCARRNoneNoneNoneNoneNone20152457295.527323577.0Arson2457296.5274.000561.00B40.588583-123.06961713.0STATE OR PRIVATECANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xb8\x8f\xdc\x9at\xc4^\xc0\xa8\xfd\x0f\xb0VKD@\xb8\x8f\xdc\x9at\xc4^\xc0\xa8\xfd\x0f\xb0VKD@|\x01\x00\x00\x00\xb8\x8f\xdc\x9at\xc4^\xc0\xa8\xfd\x0f\xb0VKD@\xfe'
188045818804593003483612015CAIRS27957490NONFEDST-CACDFST/C&LUSCAHUUHumboldt-Del Norte UnitCAHUUHumboldt-Del Norte Unit599566005748None1-64NoneNoneNoneNoneNone20152457235.521313311.0Lightning2457240.5218.010004.00B40.244833-123.54416715.0UNDEFINED FEDERALCANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xfc#\xd3\xa1\xd3\xe2^\xc0\xa8\xfd\x0f\xb0V\x1fD@\xfc#\xd3\xa1\xd3\xe2^\xc0\xa8\xfd\x0f\xb0V\x1fD@|\x01\x00\x00\x00\xfc#\xd3\xa1\xd3\xe2^\xc0\xa8\xfd\x0f\xb0V\x1fD@\xfe'
188045918804603003483622015CAIRS28291374NONFEDST-CACDFST/C&LUSCALNUSonoma-Lake Napa UnitCALNUSonoma-Lake Napa Unit590768004397NoneBENNETTNoneNoneNoneNoneNone20152457170.514814209.0Miscellaneous2457170.5148.014360.50B38.415608-122.66004413.0STATE OR PRIVATECANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xf0z0)>\xaa^\xc0h\xfa\x97\xa425C@\xf0z0)>\xaa^\xc0h\xfa\x97\xa425C@|\x01\x00\x00\x00\xf0z0)>\xaa^\xc0h\xfa\x97\xa425C@\xfe'
188046018804613003483632015CAIRS29019636NONFEDST-CACDFST/C&LUSCASHUShasta-Trinity UnitCASHUShasta-Trinity Unit591814009371NoneODESSA 2NoneNoneNoneNoneNone20152457291.5269172613.0Missing/Undefined2457291.5269.018430.01A40.481637-122.38937513.0STATE OR PRIVATECANoneNoneNoneb'\x00\x01\xad\x10\x00\x00P\xb8\x1e\x85\xeb\x98^\xc0\x98\xc5\xfdG\xa6=D@P\xb8\x1e\x85\xeb\x98^\xc0\x98\xc5\xfdG\xa6=D@|\x01\x00\x00\x00P\xb8\x1e\x85\xeb\x98^\xc0\x98\xc5\xfdG\xa6=D@\xfe'
188046118804623003483732015CAIRS29217935NONFEDST-CACDFST/C&LUSCATCUTuolumne-Calaveras UnitCATCUTuolumne-Calaveras Unit569419000366NoneNoneNoneNoneNoneNoneNone20152457300.527801269.0MiscellaneousNaNNaNNone0.20A37.617619-120.93857012.0MUNICIPAL/LOCALCANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\x00\x80\xbe\x88\x11<^\xc0\xa8\xca\x06%\x0e\xcfB@\x00\x80\xbe\x88\x11<^\xc0\xa8\xca\x06%\x0e\xcfB@|\x01\x00\x00\x00\x00\x80\xbe\x88\x11<^\xc0\xa8\xca\x06%\x0e\xcfB@\xfe'
188046218804633003483752015CAIRS28364460NONFEDST-CACDFST/C&LUSCATCUTuolumne-Calaveras UnitCATCUTuolumne-Calaveras Unit574245000158NoneNoneNoneNoneNoneNoneNone20152457144.5122205213.0Missing/UndefinedNaNNaNNone0.10A37.617619-120.93857012.0MUNICIPAL/LOCALCANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\x00\x80\xbe\x88\x11<^\xc0\xa8\xca\x06%\x0e\xcfB@\x00\x80\xbe\x88\x11<^\xc0\xa8\xca\x06%\x0e\xcfB@|\x01\x00\x00\x00\x00\x80\xbe\x88\x11<^\xc0\xa8\xca\x06%\x0e\xcfB@\xfe'
188046318804643003483772015CAIRS29218079NONFEDST-CACDFST/C&LUSCATCUTuolumne-Calaveras UnitCATCUTuolumne-Calaveras Unit570462000380NoneNoneNoneNoneNoneNoneNone20152457309.5287230913.0Missing/UndefinedNaNNaNNone2.00B37.672235-120.89835612.0MUNICIPAL/LOCALCANoneNoneNoneb'\x00\x01\xad\x10\x00\x00x\xba_\xaa~9^\xc0\xb8dL\xc9\x0b\xd6B@x\xba_\xaa~9^\xc0\xb8dL\xc9\x0b\xd6B@|\x01\x00\x00\x00x\xba_\xaa~9^\xc0\xb8dL\xc9\x0b\xd6B@\xfe'
188046418804653003483992015CAIRS26733926NONFEDST-CACDFST/C&LUSCABDUSan Bernardino UnitCABDUCDF - San Bernardino Unit535436003225NoneBARKER BL BIG_BEAR_LAKE_NoneNoneNoneNoneNone20152457095.57321289.0MiscellaneousNaNNaNNone0.10A34.263217-116.83095013.0STATE OR PRIVATECANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\x1c\xa7\xe8H.5]\xc00`;\x18\xb1!A@\x1c\xa7\xe8H.5]\xc00`;\x18\xb1!A@|\x01\x00\x00\x00\x1c\xa7\xe8H.5]\xc00`;\x18\xb1!A@\xfe'